Uncovering Customer Insights With Predictive Modeling & Big Data

Uncovering Customer Insights

Market intelligence, marketing analyst, customer insights and customer analytics – these have all emerged as new popular “buzz” professions a few years ago. Falling somewhere between “data” and “business”, these functions are meant to intelligently steer business decisions to where the real opportunity lies. Analytically-driven, but pragmatic in nature, those in customer insights and market intelligence rely on quantitative and qualitative data to give direction to companies’ stakeholders and – at their best – provide vision on how to best proceed in a given situation.

Backed up with data and rationale, these roles are meant to answer questions like:

  • What is the opportunity size for ”Product A” versus “Product B”?
  • What market should we go after with this product and why (industry, company size, country, persona…)?
  • What go-to-market approach is appropriate for this product (sales, channel, marketing mix…)?
  • How can we grow this product line?

Years ago when I first got a job in market intelligence at Autodesk – alongside three other fresh and hungry MBA grads – I was fortunate enough to be mentored by a smart and business-driven individual.

During our first week of training we were given a weekend exercise: create a business profile of four different customers of our choice. “Be creative”, he encouraged. “What would be important to know about a customer to grow the business with them?”

The exercise turned out to be much more time-consuming than I thought. I recall complaining about losing over five hours of a Sunday afternoon. On Monday morning the four of us – a bit overly eager to succeed – presented the results to the team. The profiles that we created looked something like this:

Company Profile Example

It took me hours to research every single company by going to a myriad of websites to answer the following questions:

  • How many people work at the company?
  • How many of them engineers?
  • Are they using competitive products?
  • What other technologies are they using?
  • Are they growing, investing, hiring…?

Being new to the industry, it was even more difficult to answer questions like:

  • What are their pain points?
  • What should we be talking to them about?
  • What channels should we use to engage with them?

As my mentor promised, however, the exercise did teach me a lot. I learned how to profile a customer from a business perspective, understand their pains, and formulate our value proposition.

Though the intelligence we provided was anecdotal (how many companies could we really manually profile?), for the first time it was clear to me how to start engaging with the customers in a more meaningful way. The effect our research exercise had on our marketing stakeholders was eye-opening.

Today with many companies that crawl the web and automate this kind of intelligence “hunting” process (such as Mintigo), you can get your customer data enriched with hundreds of relevant data points (financial growth, talent being hired, technologies being used, strategic areas of focus and investment) in a matter of minutes. Automation of data gathering today saves analysts days and sometimes months. However, we still need to answer the much needed question of “So What”?

The role of “intelligence” could not be more relevant in this age of big data, machine-learning, and sales and marketing automation. But it still requires a human touch to connect the dots between your products’ usage patterns and possible partnership opportunities, detect sweet spots for competitive displacement campaigns, and point out new markets to go after. It’s just that this is all made much easier today with technology and the vast amount of data available.

To me, translating data into something actionable and empowering others with knowledge has always been the perk of the job. During one of my recent customer engagements, I spent a few days analyzing the customer’s data enriched with a wealth of additional information provided by Mintigo, such as technologies that their customers were using, types of skillsets they were hiring for, etc. It became quickly evident that there were three very distinct groups of customers that they were selling to, something that was not obvious to the customer previously due to their lack of comprehensive customer data at the time. Hearing from the customer that we “confirmed their go-to-market strategy that took them two years to formulate” is very rewarding.

Of course, amazing insights and business strategies need leadership and follow-through to become meaningful and impact the business.

However, with today’s access to the right data, robust analytics, and predictive modeling capabilities at their disposal, analysts can provide the level of insights that could drive the direction of any company.


[ebook] Why B2B Marketers Need Predictive Marketing

Download the free ebook: "Why Modern B2B Marketers Need Predictive Marketing"

Predictive marketing is rapidly becoming something that every company needs to survive. Learn how industry leaders are implementing predictive marketing and what results they've seen.

Download Now



Lena Redko


Lena is leading the Customer Insights and Analytics practice in Mintigo. She has over 10 years of experience in market intelligence & analytics, strategy development, and tactical execution. Her passion lies in transforming the data into actionable insights and acting on them through go-to-market and execution support. Prior to Mintigo, Lena worked in various analytics roles at Cisco, Ernst & Young, and Autodesk.